scholarly journals Path planning for sensor data collecting mobile robot

2005 ◽  
Author(s):  
P.N. Pathirana ◽  
T.J. Black ◽  
S. Nahavandi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Sensor Data ◽  
Data Collecting

scholarly journals A dynamic path planning method for wheeled mobile robots (Dyna-bug).

2017 ◽  
Vol 7 (3) ◽  
pp. 123-128
Author(s):  
Suat Karakaya ◽  
Gurkan Kucukyildiz ◽  
Hasan Ocak
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Cost Function ◽  
Sensor Data ◽  
Stationary Condition ◽  
Wheeled Mobile Robots ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
The Cost ◽  
Local Map

Abstract   In this study, a hybrid path-planning scheme is presented. The main contribution of this paper is merging the static grid costs of the global map and the immediate environmental structure of the local map. The stationary condition of the map and the instant local goal is weighted by certain coefficients in order to determine the next move of the wheeled mobile robot (WMR). Thus, the cost function is defined in terms of the grid costs and the dynamic parameters. The main assumption is that the WMR on which this scheme is executed must be equipped with a field scanning sensor. The sensor readings in each processing cycle are pre-processed before plugging in the cost function. The passages in the local map are extracted from the sensor data, then the optimal collision-free point lying on the passages is obtained via the cost function. Keywords: Path planning, collision avoidance, mobile robot.  

Topology optimization techniques for mobile robot path planning

2019 ◽  
Vol 78 ◽  
pp. 528-544 ◽  
Author(s):  
Baotong Li ◽  
Honglei Liu ◽  
Wenjun Su
Keyword(s):  
Topology Optimization ◽  
Path Planning ◽  
Mobile Robot ◽  
Optimization Techniques ◽  
Robot Path Planning ◽  
Robot Path

scholarly journals Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110264
Author(s):  
Jiqing Chen ◽  
Chenzhi Tan ◽  
Rongxian Mo ◽  
Hongdu Zhang ◽  
Ganwei Cai ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Path Length ◽  
Search Time ◽  
Experimental Results ◽  
Artificial Potential Field ◽  
A Algorithm ◽  
Neighbor Search

Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

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